Abstract: With the increasing complexity of emergencies related to the community correction object, the single fixed plan from the existing emergency plan database is unable to develop intelligent emergency response plans for different abnormal situations through dynamic data injection, which can hardly meet the demands of dynamic emergency generation. To improve the supervision quality and the informational management level of community correction object, we adopt the joint mining technology of multi-source heterogeneous data, in view of multi-source heterogeneity, complex association and dynamic evolution of abnormal situation data. On this basis, we build our judicial Knowledge Graph (KGjudicial) and crime Event Logic Graph (ELGcrime), providing data basis and auxiliary decision support for the dynamic generation of intelligent emergency plans. In addition, considering the actual business requirements for cross-regional multi-sectoral emergency coordination, we explore the multi-department information alignment method and the dynamic injection mechanism of emergency response plans. We propose the fusion technology of multiple-department emergency response plans based on our KGjudicial and ELGcrime to realize the cross-regional joint law enforcement of judicial administration departments and improve the supervision quality, while saving the management cost of community correction object. We provide technical support for the emergency response of multiple departments of judicial administration, contributing to the social security and stability.
Abstract: This study summarizes the current research on semantic-based video retrieval to help future researchers understand the technologies available in this field, and video retrieval systems are created to find the video that users want to query in a large number of video data collections on the Internet or in databases. This study introduces and discusses the semantic-based video retrieval process and also summarizes the relevant techniques to solve the main problem of a semantic gap in this process. The semantic gap is induced by the difference between the low-level features extracted from video content and the user’s cognition of these features in the real world. It is a highly concerned research topic to transform the low-level features of video content into high-level semantic concepts.
Abstract: In recent years, the quantitative investment models based on artificial intelligence algorithms have been emerging in the field of quantitative finance. These models attempt to model the financial time series through artificial intelligence methods, thereby forecasting data and developing an investment strategy. Regarding the unreliable prediction of the traditional Long Short Term Memory (LSTM) model for financial time series, we propose an improved LSTM model. The attention mechanism is added into the LSTM layer to enhance the forecasting performance of the neural network, and the Genetic Algorithm (GA) is used to optimize parameters, thus improving the model’s generalization ability. The data of China’s stock indexes and futures from the January 2019 to May 2020 is selected for the comparative experiments with state-of-the-art algorithms. The results show that the improved model performs better than other models in every indicators, proving the effect application of the model to future investment.
Abstract: Building energy-saving control is a multi-objective optimization problem considering the comfort demand. However, for the new buildings lacking operation data, it is a real conundrum to control the Heating, Ventilation and Air-Conditioning (HVAC) system to achieve both comfort and energy-saving. Aiming at this problem, this study first builds the space model of new buildings and then carries out simulation of energy consumption on the model. On this basis, it puts forward a fuzzy control algorithm based on thermal comfort of personnel to determine the optimal operation interval. Therefore, longer days of thermal comfort are enabled under the condition of lower energy consumption, achieving the goal of both energy saving and comfort. The energy-saving control based on the thermal comfort of personnel can promote the green operation of HVAC systems in buildings.
Abstract: The study of crystal structure is the basis for studying the physical and chemical properties of solid materials, and the screening of crystal structure is usually based on the principle of least energy. The use of density functional theory to calculate the structure energy requires a lot of computing resources and service time. For this reason, this research proposes a deep learning method for material structure prediction to speed up the prediction of material crystal structure. This work systematically studied and analyzed the data set optimization, training method, algorithm optimization, and so on. The network parameters and optimized algorithm of deep learning for crystal structure prediction are confirmed and coded. The optimized deep learning method is used to find out stable structure of Silicon, titanium dioxide, and perovskite CaTiO3, the predicted structures are well agreement with the experimental results.
Abstract: To improve the racing performance of intelligent cars, this study introduces an intelligent racing car system based on IMXRT1021 with regard to selection of key components, design of hardware and circuit boards, and processing of sensor signals, as well as assembly, algorithms and control. This system consists of mechanical and hardware parts, PCB design, sensor signal processing, the recognition algorithm of racing track elements, control strategy and software design architecture. This study experimentally elaborates the recognition and control schemes of each racing elements. It compares the driving trajectories and absolute velocities of intelligent cars and analyzes the influence of different control algorithms on vital technical specifications such as finish time and stability. This design scheme shows its advantages in accurate control, sensitive steering and careful route planning, providing a sound reference for the students who are preparing for the four-wheel group in the National University Students Intelligent Car Race.
Abstract: With the continuous development of digital twin technology at this stage, research and applications surrounding digital twins have gradually become a hot spot. Because traditional automated driving test methods have various defects in terms of functionality, safety, and test cost, this article proposes a digital twin automatic driving test method based on the basic characteristics of the digital twin and the test method of autonomous driving. The method of constructing the driving test environment uses spatial coordinate mapping, collision detection model, and virtual scene registration to map the automatic driving information in the actual environment to the virtual scene. At the same time, the corresponding mixed reality automatic driving test model is constructed and passed the experiment. The collision test with interactive features of the mixed reality system is shown. The performance of the system at sampling frequencies of 50ms, 200ms and 1000ms is compared and analyzed. Experiments show that the algorithm in this paper has better operating frame rate characteristics at the sampling frequency of 200ms or above.
Abstract: Soybeans include many varieties (cultivars) and their cultivars have very subtle differences in leaf patterns which makes it very tough to distinguish them from leaf features. Great progress has been made on using leaf image patterns for plant species recognition. However, as a general very fined-grained pattern recognition problem, soybean cultivar recognition has not yet received considerable attention. Traditional handcrafted leaf image analysis methods are limited to capture the subtle differences of leaf features among different cultivars. In this paper, we make the attempt of using deep learning to harvest discriminatory leaf features for soybean cultivar recognition. A novel deep learning model, named transformation attention network (TAN), is proposed in this work. It first focuses on extracting fine-grained leaf features via attention mechanism and then rectifies the leaf posture using affine transformations. We constructed a soybean leaf cultivar dataset which consists of 240 soybean cultivars with 10 samples per cultivar to examine the availability of cultivar information in leaf patterns and validate the effectiveness of the proposed deep learning model for soybean cultivar recognition. The encouraging experimental results confirm the effectiveness of leaf image patterns for distinguishing cultivars and demonstrate the better performance of the proposed method over the state-of-the-art handcrafted methods and deep learning methods for soybean cultivar recognition.
Abstract: In order to analyze the research status, development trend and research hotspots in the field of source address verification in China, and sort out the development trend of source address verification research, so as to promote the further research on trusted transmission of national network data. This paper takes the papers and literatures based on source address verification in CNKI database as the research data source, applies bib-liometrics and scientific knowledge map, and uses CiteSpace as a visual tool to carry out information statistics, co-citation statistics and cluster analysis on the research samples, and draws the inter-annual variation map of the literature in this research field and the knowledge map of co-occurrence clustering and time-series dis-tribution, so as to make scientific analysis. The research shows that the research of source address verification in China tends to develop dynamically and the trend is stable and good; Core research strength: headed by Professor Wu Jianping, Jun Bi and Xu Wei as important research experts and headed by Tsinghua University, PLA Information Engineering University and Chinese Academy of Sciences as important research institutions; Next generation Internet and software-defined network are important emerging research hotspots, which reflect the future research direction and development trend of source address verification research.
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